Evolution of cortical geometry and its link to function, behaviour and ecology

Studies in comparative neuroanatomy and of the fossil record demonstrate the influence of socio-ecological niches on the morphology of the cerebral cortex, but have led to oftentimes conflicting theories about its evolution. Here, we study the relationship between the shape of the cerebral cortex and the topography of its function. We establish a joint geometric representation of the cerebral cortices of ninety species of extant Euarchontoglires, including commonly used experimental model organisms. We show that variability in surface geometry relates to species’ ecology and behaviour, independent of overall brain size. Notably, ancestral shape reconstruction of the cortical surface and its change during evolution enables us to trace the evolutionary history of localised cortical expansions, modal segregation of brain function, and their association to behaviour and cognition. We find that individual cortical regions follow different sequences of area increase during evolutionary adaptations to dynamic socio-ecological niches. Anatomical correlates of this sequence of events are still observable in extant species, and relate to their current behaviour and ecology. We decompose the deep evolutionary history of the shape of the human cortical surface into spatially and temporally conscribed components with highly interpretable functional associations, highlighting the importance of considering the evolutionary history of cortical regions when studying their anatomy and function.


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Policy information about availability of of data All manuscripts must include a data availability statement This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or or web links for publicly available datasets -A description of of any restrictions on on data availability -For clinical datasets or or third party data, please ensure that the statement adheres to to our policy Prof. Georg Langs Feb 21, 2023 No No software was used for data collection.
All analysis was implemented in in Matlab R2014a and R2019a, Python 2.7.13 and 3.7.3 as as well as as R 3.6.0 and 4.0.3. Additional processing was performed using Convert 3D 3D 1.1.0, FSL 6.0.4, FreeSurfer 6.0.0 and 7.1.1, ANTs 2.3.4 and ImageJ 1.49u. Computer codes for individual processing and/or evaluation steps are available from the corresponding author upon reasonable request.
All quantitative data supporting the findings of of this study are provided as as supplementary information to to the article. Sources of of all imaging data used in in the study as as well as as reference publications are available in in Supplementary Data 1. 1. Aligned surface models used to to define the proposed common reference frame, as as well as as ancestral state estimates obtained from it it are publicly available at at https://github.com/cirmuw/EvolutionOfCorticalShape. Expansion maps used for meta-analytic nature portfolio | reporting summary

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The results of this study are based on volumetric imaging data obtained via various modalities (detailed in the supplementary materials) from 90 individual species of Primates, Rodents, Lagomorphs, Dermoptera and Scandentia. They represent the maximum number of species that the authors could assemble from third parties. The sample size was not determined a priori Data of the following species was collected but excluded due to strong distortions in the morphology of the brains (deterioration due to long preservation, inadequate container sizes): Ateles fusciceps, Cercopithecus hamlyni, Erythrocebus patas, Macaca nemestrina, Mandrillus sphinx, Pithecia pithecia (all from the "Primate Brain Bank" dataset doi:10.1159/000488136) Unfortunately, no data for the replication of the study is available at the moment, as no collection of imaging data from a comparable number of species in the same phylogeny has been collected to date.
Organisms were grouped based on information on preferred habitat, activity time and group size. Unfortunately potential confounds (for example age, sex, image quality) could not be accounted for due to unavailabilty of that information.
all evaluations were performed algorithmically, no blinding was necessary to avoid bias Of the 90 species used in the study, 75 were imaged using MRI. As only third party data was used in this study, design specifications varied and are described in detail in the corresponding publications listed in Supplementary Table 1 no behavioral performance was assessed in this study structural various field strengths specified in Supplementary Table 1 various imaging parameters specified in Supplementary Table 1 Whole brain scans only no preprocessing was performed Whole brain volumes were manually rotated to approximately position the AC/PC line in the axial plane. Multivariate modeling or or predictive analysis Either Pearson or or Spearman correlation (depending on on normality, determined via Kolmogorov-Smirnov tests) was used depending on on the normality of of the data when assessing relationship between any two variables that are spatially independent. Surrogate-based statistics were used to to assess the significance of of results in in specific cortical parcellations (eg. Burt, J. J. B., Helmer, M., Shinn, M., Anticevic, A. A. & Murray, J. J. D. D. Generative modeling of of brain maps with spatial autocorrelation. Neuroimage 220, 117038 (2020) Psychol. 8, 8, 456 (2017).) was used to to assess the stability of of species habitat on on the relative expansion of of individual cortical areas (Supplementary Table 7c) Depending on on value distribution (assessed via Kolmogorov-Smirnov tests), Wilcoxon-Rank-Sum/Mann-Whitney U-Test or or twosample t-tests were used to to assess the significance of of differences in in range parameter of of spatial statistical models of of modal specificity (see Supplementary Methods, Supplementary Table 8a) Linear modelling was used to to assess the relationship between evolutionary change in in this range parameter and deep time in in the human lineag (Supplementary Table 8b) Pearson correlation was used to to assess the relationship between progression of of meta-analytical term decodings of of evolutionary cortical surface expansion and estimates of of ancestral likelihoods of of socio-ecological factors (Supplementary  Table 9a). Partial correlation analysis of of these values (controlling for diurnality) was performed with confidence intervals determined by by 10000 bootstrap iterations (Supplementary Table 9b) no no task or or stimulus conditions were tested in in this study no no statistical inference was performed in in this study FDR correction was performed to to correct for multiple comparisons. Phylogeny was accounted for in in all computations.